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014-microsoft-deberta-v3-base-finetuned-yahoo-80_20

Text Classification
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deberta-v2
Generated from Trainer
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+ ---
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+ license: mit
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+ base_model: microsoft/deberta-v3-base
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - f1
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+ - accuracy
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+ - precision
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+ - recall
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+ model-index:
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+ - name: 014-microsoft-deberta-v3-base-finetuned-yahoo-80_20
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+ results: []
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # 014-microsoft-deberta-v3-base-finetuned-yahoo-80_20
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+
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+ This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 3.3983
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+ - F1: 0.2344
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+ - Accuracy: 0.3
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+ - Precision: 0.2369
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+ - Recall: 0.3
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+ - System Ram Used: 4.9456
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+ - System Ram Total: 83.4807
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+ - Gpu Ram Allocated: 4.8430
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+ - Gpu Ram Cached: 7.0469
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+ - Gpu Ram Total: 39.5640
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+ - Gpu Utilization: 11
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+ - Disk Space Used: 40.4033
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+ - Disk Space Total: 78.1898
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 2e-05
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+ - train_batch_size: 8
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+ - eval_batch_size: 8
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+ - seed: 42
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - num_epochs: 50
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy | Precision | Recall | System Ram Used | System Ram Total | Gpu Ram Allocated | Gpu Ram Cached | Gpu Ram Total | Gpu Utilization | Disk Space Used | Disk Space Total |
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+ |:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|:---------:|:------:|:---------------:|:----------------:|:-----------------:|:--------------:|:-------------:|:---------------:|:---------------:|:----------------:|
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+ | 2.311 | 2.5 | 25 | 2.3033 | 0.0857 | 0.15 | 0.1105 | 0.15 | 5.0008 | 83.4807 | 4.8431 | 7.0469 | 39.5640 | 40 | 36.2797 | 78.1898 |
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+ | 2.2703 | 5.0 | 50 | 2.3011 | 0.0686 | 0.2 | 0.0417 | 0.2 | 5.0121 | 83.4807 | 4.8431 | 7.0469 | 39.5640 | 38 | 36.2797 | 78.1898 |
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+ | 2.0062 | 7.5 | 75 | 2.2817 | 0.0794 | 0.15 | 0.0543 | 0.15 | 4.9856 | 83.4807 | 4.8431 | 7.0469 | 39.5640 | 42 | 36.2797 | 78.1898 |
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+ | 1.49 | 10.0 | 100 | 2.3281 | 0.1178 | 0.2 | 0.0869 | 0.2 | 4.9824 | 83.4807 | 4.8431 | 7.0469 | 39.5640 | 37 | 36.2797 | 78.1898 |
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+ | 0.9424 | 12.5 | 125 | 2.3475 | 0.1733 | 0.25 | 0.1417 | 0.25 | 4.9446 | 83.4807 | 4.8431 | 7.0469 | 39.5640 | 42 | 36.2798 | 78.1898 |
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+ | 0.5591 | 15.0 | 150 | 2.4503 | 0.1744 | 0.25 | 0.1452 | 0.25 | 4.9201 | 83.4807 | 4.8431 | 7.0469 | 39.5640 | 29 | 36.2798 | 78.1898 |
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+ | 0.2893 | 17.5 | 175 | 2.5557 | 0.1744 | 0.25 | 0.1452 | 0.25 | 4.9618 | 83.4807 | 4.8431 | 7.0469 | 39.5640 | 43 | 36.2798 | 78.1898 |
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+ | 0.1623 | 20.0 | 200 | 2.6218 | 0.2411 | 0.3 | 0.2452 | 0.3 | 4.9110 | 83.4807 | 4.8431 | 7.0469 | 39.5640 | 30 | 36.2799 | 78.1898 |
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+ | 0.0817 | 22.5 | 225 | 2.7346 | 0.24 | 0.3 | 0.2417 | 0.3 | 4.9413 | 83.4807 | 4.8431 | 7.0469 | 39.5640 | 38 | 36.2799 | 78.1898 |
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+ | 0.0475 | 25.0 | 250 | 2.9325 | 0.2344 | 0.3 | 0.2369 | 0.3 | 4.9314 | 83.4807 | 4.8431 | 7.0469 | 39.5640 | 40 | 36.2800 | 78.1898 |
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+ | 0.0322 | 27.5 | 275 | 3.1235 | 0.2511 | 0.3 | 0.2869 | 0.3 | 4.9336 | 83.4807 | 4.8431 | 7.0469 | 39.5640 | 42 | 36.2800 | 78.1898 |
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+ | 0.0254 | 30.0 | 300 | 3.1455 | 0.2344 | 0.3 | 0.2369 | 0.3 | 4.9387 | 83.4807 | 4.8431 | 7.0469 | 39.5640 | 30 | 36.2800 | 78.1898 |
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+ | 0.0195 | 32.5 | 325 | 3.2767 | 0.2344 | 0.3 | 0.2369 | 0.3 | 4.9198 | 83.4807 | 4.8431 | 7.0469 | 39.5640 | 38 | 36.2801 | 78.1898 |
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+ | 0.0163 | 35.0 | 350 | 3.3281 | 0.2344 | 0.3 | 0.2369 | 0.3 | 4.9709 | 83.4807 | 4.8431 | 7.0469 | 39.5640 | 42 | 40.4031 | 78.1898 |
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+ | 0.015 | 37.5 | 375 | 3.3318 | 0.2344 | 0.3 | 0.2369 | 0.3 | 4.9642 | 83.4807 | 4.8431 | 7.0469 | 39.5640 | 41 | 40.4032 | 78.1898 |
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+ | 0.0133 | 40.0 | 400 | 3.3617 | 0.2511 | 0.3 | 0.2869 | 0.3 | 4.9608 | 83.4807 | 4.8431 | 7.0469 | 39.5640 | 37 | 40.4032 | 78.1898 |
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+ | 0.0127 | 42.5 | 425 | 3.3788 | 0.2344 | 0.3 | 0.2369 | 0.3 | 4.9617 | 83.4807 | 4.8431 | 7.0469 | 39.5640 | 39 | 40.4032 | 78.1898 |
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+ | 0.0129 | 45.0 | 450 | 3.3928 | 0.2511 | 0.3 | 0.2869 | 0.3 | 4.9576 | 83.4807 | 4.8431 | 7.0469 | 39.5640 | 41 | 40.4032 | 78.1898 |
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+ | 0.0121 | 47.5 | 475 | 3.3897 | 0.2344 | 0.3 | 0.2369 | 0.3 | 4.9421 | 83.4807 | 4.8431 | 7.0469 | 39.5640 | 37 | 40.4033 | 78.1898 |
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+ | 0.0124 | 50.0 | 500 | 3.3983 | 0.2344 | 0.3 | 0.2369 | 0.3 | 4.9581 | 83.4807 | 4.8431 | 7.0469 | 39.5640 | 38 | 40.4033 | 78.1898 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.31.0
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+ - Pytorch 2.0.1+cu118
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+ - Datasets 2.13.1
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+ - Tokenizers 0.13.3